1 Data preparation

1.1 Outline

  • Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded

  • Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.

1.2 Load packages


library(reportfactory)
library(here)
library(rio) 
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)

1.3 Load scripts

These scripts will load:

  • all scripts stored as .R files inside /scripts/
  • all scripts stored as .R files inside /src/

These scripts also contain routines to access the latest clean encrypted data (see next section).


reportfactory::rfh_load_scripts()

1.4 Load clean data

We import the latest NHS pathways data:


x <- import_pathways() %>%
  as_tibble()
x
## # A tibble: 125,165 x 9
##    site_type date       sex    age   ccg_code ccg_name count postcode nhs_region
##    <chr>     <date>     <chr>  <chr> <chr>    <chr>    <int> <chr>    <chr>     
##  1 111       2020-03-18 female 0-18  e380000… nhs_bar…    35 rm13ae   london    
##  2 111       2020-03-18 female 0-18  e380000… nhs_bed…    27 mk454hr  east_of_e…
##  3 111       2020-03-18 female 0-18  e380000… nhs_bla…     9 bb12fd   north_west
##  4 111       2020-03-18 female 0-18  e380000… nhs_bro…    11 br33ql   london    
##  5 111       2020-03-18 female 0-18  e380000… nhs_can…     9 ws111jp  midlands  
##  6 111       2020-03-18 female 0-18  e380000… nhs_cit…    12 n15lz    london    
##  7 111       2020-03-18 female 0-18  e380000… nhs_enf…     7 en40dy   london    
##  8 111       2020-03-18 female 0-18  e380000… nhs_ham…     6 dl62uu   north_eas…
##  9 111       2020-03-18 female 0-18  e380000… nhs_har…    24 ts232la  north_eas…
## 10 111       2020-03-18 female 0-18  e380000… nhs_kin…     6 kt11eu   london    
## # … with 125,155 more rows

We also import demographics data for NHS regions in England, used later in our analysis:


path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
##                  nhs_region variable      value
## 1                North West     0-18 0.22538599
## 2  North East and Yorkshire     0-18 0.21876449
## 3                  Midlands     0-18 0.22564656
## 4           East of England     0-18 0.22810783
## 5                    London     0-18 0.23764782
## 6                South East     0-18 0.22458811
## 7                South West     0-18 0.20799797
## 8                North West    19-69 0.64274078
## 9  North East and Yorkshire    19-69 0.64437753
## 10                 Midlands    19-69 0.63876675
## 11          East of England    19-69 0.63034229
## 12                   London    19-69 0.67820084
## 13               South East    19-69 0.63267336
## 14               South West    19-69 0.63176131
## 15               North West   70-120 0.13187323
## 16 North East and Yorkshire   70-120 0.13685797
## 17                 Midlands   70-120 0.13558669
## 18          East of England   70-120 0.14154988
## 19                   London   70-120 0.08415135
## 20               South East   70-120 0.14273853
## 21               South West   70-120 0.16024072

Finally, we import publically available deaths per NHS region:


dth <- import_deaths() %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

#truncation to account for reporting delay
delay_max <- 21

dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
##     date_report               nhs_region deaths
## 1    2020-03-01          East of England      0
## 2    2020-03-02          East of England      1
## 3    2020-03-03          East of England      0
## 4    2020-03-04          East of England      0
## 5    2020-03-05          East of England      0
## 6    2020-03-06          East of England      1
## 7    2020-03-07          East of England      0
## 8    2020-03-08          East of England      0
## 9    2020-03-09          East of England      1
## 10   2020-03-10          East of England      0
## 11   2020-03-11          East of England      0
## 12   2020-03-12          East of England      0
## 13   2020-03-13          East of England      1
## 14   2020-03-14          East of England      2
## 15   2020-03-15          East of England      2
## 16   2020-03-16          East of England      1
## 17   2020-03-17          East of England      1
## 18   2020-03-18          East of England      5
## 19   2020-03-19          East of England      4
## 20   2020-03-20          East of England      2
## 21   2020-03-21          East of England     11
## 22   2020-03-22          East of England     11
## 23   2020-03-23          East of England     11
## 24   2020-03-24          East of England     19
## 25   2020-03-25          East of England     26
## 26   2020-03-26          East of England     36
## 27   2020-03-27          East of England     38
## 28   2020-03-28          East of England     28
## 29   2020-03-29          East of England     42
## 30   2020-03-30          East of England     45
## 31   2020-03-31          East of England     70
## 32   2020-04-01          East of England     61
## 33   2020-04-02          East of England     64
## 34   2020-04-03          East of England     80
## 35   2020-04-04          East of England     71
## 36   2020-04-05          East of England     76
## 37   2020-04-06          East of England     71
## 38   2020-04-07          East of England     92
## 39   2020-04-08          East of England    111
## 40   2020-04-09          East of England     87
## 41   2020-04-10          East of England     74
## 42   2020-04-11          East of England     91
## 43   2020-04-12          East of England    101
## 44   2020-04-13          East of England     77
## 45   2020-04-14          East of England     61
## 46   2020-04-15          East of England     82
## 47   2020-04-16          East of England     74
## 48   2020-04-17          East of England     86
## 49   2020-04-18          East of England     63
## 50   2020-04-19          East of England     66
## 51   2020-04-20          East of England     66
## 52   2020-04-21          East of England     74
## 53   2020-04-22          East of England     66
## 54   2020-04-23          East of England     49
## 55   2020-04-24          East of England     65
## 56   2020-04-25          East of England     54
## 57   2020-04-26          East of England     48
## 58   2020-04-27          East of England     46
## 59   2020-04-28          East of England     58
## 60   2020-04-29          East of England     32
## 61   2020-04-30          East of England     44
## 62   2020-05-01          East of England     49
## 63   2020-05-02          East of England     29
## 64   2020-05-03          East of England     41
## 65   2020-05-04          East of England     19
## 66   2020-05-05          East of England     35
## 67   2020-05-06          East of England     28
## 68   2020-05-07          East of England     33
## 69   2020-05-08          East of England     30
## 70   2020-05-09          East of England     26
## 71   2020-05-10          East of England     21
## 72   2020-05-11          East of England     18
## 73   2020-05-12          East of England     21
## 74   2020-05-13          East of England     27
## 75   2020-05-14          East of England     25
## 76   2020-05-15          East of England     19
## 77   2020-05-16          East of England     26
## 78   2020-05-17          East of England     17
## 79   2020-05-18          East of England     23
## 80   2020-05-19          East of England     15
## 81   2020-05-20          East of England     25
## 82   2020-05-21          East of England     20
## 83   2020-05-22          East of England     13
## 84   2020-05-23          East of England     12
## 85   2020-05-24          East of England     16
## 86   2020-05-25          East of England     18
## 87   2020-05-26          East of England     12
## 88   2020-05-27          East of England     10
## 89   2020-05-28          East of England      6
## 90   2020-03-01                   London      0
## 91   2020-03-02                   London      0
## 92   2020-03-03                   London      0
## 93   2020-03-04                   London      0
## 94   2020-03-05                   London      0
## 95   2020-03-06                   London      1
## 96   2020-03-07                   London      1
## 97   2020-03-08                   London      0
## 98   2020-03-09                   London      1
## 99   2020-03-10                   London      0
## 100  2020-03-11                   London      7
## 101  2020-03-12                   London      6
## 102  2020-03-13                   London     10
## 103  2020-03-14                   London     14
## 104  2020-03-15                   London     10
## 105  2020-03-16                   London     17
## 106  2020-03-17                   London     25
## 107  2020-03-18                   London     31
## 108  2020-03-19                   London     25
## 109  2020-03-20                   London     45
## 110  2020-03-21                   London     50
## 111  2020-03-22                   London     54
## 112  2020-03-23                   London     63
## 113  2020-03-24                   London     86
## 114  2020-03-25                   London    112
## 115  2020-03-26                   London    130
## 116  2020-03-27                   London    130
## 117  2020-03-28                   London    122
## 118  2020-03-29                   London    147
## 119  2020-03-30                   London    149
## 120  2020-03-31                   London    180
## 121  2020-04-01                   London    201
## 122  2020-04-02                   London    189
## 123  2020-04-03                   London    196
## 124  2020-04-04                   London    229
## 125  2020-04-05                   London    194
## 126  2020-04-06                   London    198
## 127  2020-04-07                   London    219
## 128  2020-04-08                   London    236
## 129  2020-04-09                   London    202
## 130  2020-04-10                   London    168
## 131  2020-04-11                   London    175
## 132  2020-04-12                   London    156
## 133  2020-04-13                   London    165
## 134  2020-04-14                   London    142
## 135  2020-04-15                   London    142
## 136  2020-04-16                   London    138
## 137  2020-04-17                   London     99
## 138  2020-04-18                   London    101
## 139  2020-04-19                   London    102
## 140  2020-04-20                   London     94
## 141  2020-04-21                   London     93
## 142  2020-04-22                   London    108
## 143  2020-04-23                   London     77
## 144  2020-04-24                   London     71
## 145  2020-04-25                   London     57
## 146  2020-04-26                   London     53
## 147  2020-04-27                   London     51
## 148  2020-04-28                   London     43
## 149  2020-04-29                   London     44
## 150  2020-04-30                   London     39
## 151  2020-05-01                   London     41
## 152  2020-05-02                   London     40
## 153  2020-05-03                   London     35
## 154  2020-05-04                   London     29
## 155  2020-05-05                   London     25
## 156  2020-05-06                   London     35
## 157  2020-05-07                   London     35
## 158  2020-05-08                   London     29
## 159  2020-05-09                   London     22
## 160  2020-05-10                   London     25
## 161  2020-05-11                   London     16
## 162  2020-05-12                   London     17
## 163  2020-05-13                   London     16
## 164  2020-05-14                   London     20
## 165  2020-05-15                   London     18
## 166  2020-05-16                   London     14
## 167  2020-05-17                   London     15
## 168  2020-05-18                   London      9
## 169  2020-05-19                   London     13
## 170  2020-05-20                   London     19
## 171  2020-05-21                   London     12
## 172  2020-05-22                   London     10
## 173  2020-05-23                   London      5
## 174  2020-05-24                   London      7
## 175  2020-05-25                   London      7
## 176  2020-05-26                   London     11
## 177  2020-05-27                   London      7
## 178  2020-05-28                   London      2
## 179  2020-03-01                 Midlands      0
## 180  2020-03-02                 Midlands      0
## 181  2020-03-03                 Midlands      1
## 182  2020-03-04                 Midlands      0
## 183  2020-03-05                 Midlands      0
## 184  2020-03-06                 Midlands      0
## 185  2020-03-07                 Midlands      0
## 186  2020-03-08                 Midlands      3
## 187  2020-03-09                 Midlands      1
## 188  2020-03-10                 Midlands      0
## 189  2020-03-11                 Midlands      2
## 190  2020-03-12                 Midlands      6
## 191  2020-03-13                 Midlands      5
## 192  2020-03-14                 Midlands      4
## 193  2020-03-15                 Midlands      5
## 194  2020-03-16                 Midlands     11
## 195  2020-03-17                 Midlands      8
## 196  2020-03-18                 Midlands     13
## 197  2020-03-19                 Midlands      8
## 198  2020-03-20                 Midlands     28
## 199  2020-03-21                 Midlands     13
## 200  2020-03-22                 Midlands     31
## 201  2020-03-23                 Midlands     33
## 202  2020-03-24                 Midlands     41
## 203  2020-03-25                 Midlands     48
## 204  2020-03-26                 Midlands     64
## 205  2020-03-27                 Midlands     72
## 206  2020-03-28                 Midlands     89
## 207  2020-03-29                 Midlands     92
## 208  2020-03-30                 Midlands     90
## 209  2020-03-31                 Midlands    123
## 210  2020-04-01                 Midlands    140
## 211  2020-04-02                 Midlands    142
## 212  2020-04-03                 Midlands    124
## 213  2020-04-04                 Midlands    150
## 214  2020-04-05                 Midlands    164
## 215  2020-04-06                 Midlands    140
## 216  2020-04-07                 Midlands    123
## 217  2020-04-08                 Midlands    185
## 218  2020-04-09                 Midlands    138
## 219  2020-04-10                 Midlands    127
## 220  2020-04-11                 Midlands    142
## 221  2020-04-12                 Midlands    138
## 222  2020-04-13                 Midlands    120
## 223  2020-04-14                 Midlands    116
## 224  2020-04-15                 Midlands    147
## 225  2020-04-16                 Midlands    101
## 226  2020-04-17                 Midlands    118
## 227  2020-04-18                 Midlands    115
## 228  2020-04-19                 Midlands     91
## 229  2020-04-20                 Midlands    107
## 230  2020-04-21                 Midlands     86
## 231  2020-04-22                 Midlands     77
## 232  2020-04-23                 Midlands    102
## 233  2020-04-24                 Midlands     79
## 234  2020-04-25                 Midlands     72
## 235  2020-04-26                 Midlands     81
## 236  2020-04-27                 Midlands     74
## 237  2020-04-28                 Midlands     68
## 238  2020-04-29                 Midlands     53
## 239  2020-04-30                 Midlands     55
## 240  2020-05-01                 Midlands     64
## 241  2020-05-02                 Midlands     51
## 242  2020-05-03                 Midlands     52
## 243  2020-05-04                 Midlands     61
## 244  2020-05-05                 Midlands     58
## 245  2020-05-06                 Midlands     57
## 246  2020-05-07                 Midlands     48
## 247  2020-05-08                 Midlands     34
## 248  2020-05-09                 Midlands     37
## 249  2020-05-10                 Midlands     41
## 250  2020-05-11                 Midlands     32
## 251  2020-05-12                 Midlands     45
## 252  2020-05-13                 Midlands     38
## 253  2020-05-14                 Midlands     33
## 254  2020-05-15                 Midlands     39
## 255  2020-05-16                 Midlands     34
## 256  2020-05-17                 Midlands     30
## 257  2020-05-18                 Midlands     33
## 258  2020-05-19                 Midlands     32
## 259  2020-05-20                 Midlands     36
## 260  2020-05-21                 Midlands     31
## 261  2020-05-22                 Midlands     26
## 262  2020-05-23                 Midlands     29
## 263  2020-05-24                 Midlands     17
## 264  2020-05-25                 Midlands     24
## 265  2020-05-26                 Midlands     25
## 266  2020-05-27                 Midlands     24
## 267  2020-05-28                 Midlands      7
## 268  2020-03-01 North East and Yorkshire      0
## 269  2020-03-02 North East and Yorkshire      0
## 270  2020-03-03 North East and Yorkshire      0
## 271  2020-03-04 North East and Yorkshire      0
## 272  2020-03-05 North East and Yorkshire      0
## 273  2020-03-06 North East and Yorkshire      0
## 274  2020-03-07 North East and Yorkshire      0
## 275  2020-03-08 North East and Yorkshire      0
## 276  2020-03-09 North East and Yorkshire      0
## 277  2020-03-10 North East and Yorkshire      0
## 278  2020-03-11 North East and Yorkshire      0
## 279  2020-03-12 North East and Yorkshire      0
## 280  2020-03-13 North East and Yorkshire      0
## 281  2020-03-14 North East and Yorkshire      0
## 282  2020-03-15 North East and Yorkshire      2
## 283  2020-03-16 North East and Yorkshire      3
## 284  2020-03-17 North East and Yorkshire      1
## 285  2020-03-18 North East and Yorkshire      2
## 286  2020-03-19 North East and Yorkshire      6
## 287  2020-03-20 North East and Yorkshire      5
## 288  2020-03-21 North East and Yorkshire      6
## 289  2020-03-22 North East and Yorkshire      7
## 290  2020-03-23 North East and Yorkshire      9
## 291  2020-03-24 North East and Yorkshire      7
## 292  2020-03-25 North East and Yorkshire     18
## 293  2020-03-26 North East and Yorkshire     21
## 294  2020-03-27 North East and Yorkshire     28
## 295  2020-03-28 North East and Yorkshire     35
## 296  2020-03-29 North East and Yorkshire     38
## 297  2020-03-30 North East and Yorkshire     64
## 298  2020-03-31 North East and Yorkshire     60
## 299  2020-04-01 North East and Yorkshire     67
## 300  2020-04-02 North East and Yorkshire     74
## 301  2020-04-03 North East and Yorkshire     99
## 302  2020-04-04 North East and Yorkshire    104
## 303  2020-04-05 North East and Yorkshire     92
## 304  2020-04-06 North East and Yorkshire     95
## 305  2020-04-07 North East and Yorkshire    102
## 306  2020-04-08 North East and Yorkshire    107
## 307  2020-04-09 North East and Yorkshire    111
## 308  2020-04-10 North East and Yorkshire    117
## 309  2020-04-11 North East and Yorkshire     98
## 310  2020-04-12 North East and Yorkshire     84
## 311  2020-04-13 North East and Yorkshire     94
## 312  2020-04-14 North East and Yorkshire    107
## 313  2020-04-15 North East and Yorkshire     95
## 314  2020-04-16 North East and Yorkshire    103
## 315  2020-04-17 North East and Yorkshire     86
## 316  2020-04-18 North East and Yorkshire     95
## 317  2020-04-19 North East and Yorkshire     87
## 318  2020-04-20 North East and Yorkshire    100
## 319  2020-04-21 North East and Yorkshire     76
## 320  2020-04-22 North East and Yorkshire     83
## 321  2020-04-23 North East and Yorkshire     62
## 322  2020-04-24 North East and Yorkshire     72
## 323  2020-04-25 North East and Yorkshire     68
## 324  2020-04-26 North East and Yorkshire     63
## 325  2020-04-27 North East and Yorkshire     65
## 326  2020-04-28 North East and Yorkshire     57
## 327  2020-04-29 North East and Yorkshire     69
## 328  2020-04-30 North East and Yorkshire     56
## 329  2020-05-01 North East and Yorkshire     64
## 330  2020-05-02 North East and Yorkshire     48
## 331  2020-05-03 North East and Yorkshire     39
## 332  2020-05-04 North East and Yorkshire     48
## 333  2020-05-05 North East and Yorkshire     40
## 334  2020-05-06 North East and Yorkshire     50
## 335  2020-05-07 North East and Yorkshire     41
## 336  2020-05-08 North East and Yorkshire     38
## 337  2020-05-09 North East and Yorkshire     43
## 338  2020-05-10 North East and Yorkshire     39
## 339  2020-05-11 North East and Yorkshire     28
## 340  2020-05-12 North East and Yorkshire     25
## 341  2020-05-13 North East and Yorkshire     27
## 342  2020-05-14 North East and Yorkshire     28
## 343  2020-05-15 North East and Yorkshire     30
## 344  2020-05-16 North East and Yorkshire     35
## 345  2020-05-17 North East and Yorkshire     26
## 346  2020-05-18 North East and Yorkshire     26
## 347  2020-05-19 North East and Yorkshire     27
## 348  2020-05-20 North East and Yorkshire     21
## 349  2020-05-21 North East and Yorkshire     30
## 350  2020-05-22 North East and Yorkshire     22
## 351  2020-05-23 North East and Yorkshire     17
## 352  2020-05-24 North East and Yorkshire     22
## 353  2020-05-25 North East and Yorkshire     20
## 354  2020-05-26 North East and Yorkshire     20
## 355  2020-05-27 North East and Yorkshire     15
## 356  2020-05-28 North East and Yorkshire     10
## 357  2020-03-01               North West      0
## 358  2020-03-02               North West      0
## 359  2020-03-03               North West      0
## 360  2020-03-04               North West      0
## 361  2020-03-05               North West      1
## 362  2020-03-06               North West      0
## 363  2020-03-07               North West      0
## 364  2020-03-08               North West      1
## 365  2020-03-09               North West      0
## 366  2020-03-10               North West      0
## 367  2020-03-11               North West      0
## 368  2020-03-12               North West      2
## 369  2020-03-13               North West      3
## 370  2020-03-14               North West      1
## 371  2020-03-15               North West      4
## 372  2020-03-16               North West      2
## 373  2020-03-17               North West      4
## 374  2020-03-18               North West      6
## 375  2020-03-19               North West      6
## 376  2020-03-20               North West     10
## 377  2020-03-21               North West     11
## 378  2020-03-22               North West     13
## 379  2020-03-23               North West     15
## 380  2020-03-24               North West     21
## 381  2020-03-25               North West     20
## 382  2020-03-26               North West     29
## 383  2020-03-27               North West     35
## 384  2020-03-28               North West     27
## 385  2020-03-29               North West     46
## 386  2020-03-30               North West     66
## 387  2020-03-31               North West     52
## 388  2020-04-01               North West     85
## 389  2020-04-02               North West     95
## 390  2020-04-03               North West     94
## 391  2020-04-04               North West     98
## 392  2020-04-05               North West    102
## 393  2020-04-06               North West    100
## 394  2020-04-07               North West    133
## 395  2020-04-08               North West    125
## 396  2020-04-09               North West    119
## 397  2020-04-10               North West    117
## 398  2020-04-11               North West    137
## 399  2020-04-12               North West    126
## 400  2020-04-13               North West    126
## 401  2020-04-14               North West    131
## 402  2020-04-15               North West    114
## 403  2020-04-16               North West    134
## 404  2020-04-17               North West     97
## 405  2020-04-18               North West    113
## 406  2020-04-19               North West     70
## 407  2020-04-20               North West     82
## 408  2020-04-21               North West     76
## 409  2020-04-22               North West     83
## 410  2020-04-23               North West     85
## 411  2020-04-24               North West     65
## 412  2020-04-25               North West     65
## 413  2020-04-26               North West     54
## 414  2020-04-27               North West     54
## 415  2020-04-28               North West     56
## 416  2020-04-29               North West     62
## 417  2020-04-30               North West     59
## 418  2020-05-01               North West     43
## 419  2020-05-02               North West     55
## 420  2020-05-03               North West     55
## 421  2020-05-04               North West     44
## 422  2020-05-05               North West     47
## 423  2020-05-06               North West     41
## 424  2020-05-07               North West     45
## 425  2020-05-08               North West     41
## 426  2020-05-09               North West     28
## 427  2020-05-10               North West     38
## 428  2020-05-11               North West     32
## 429  2020-05-12               North West     36
## 430  2020-05-13               North West     24
## 431  2020-05-14               North West     26
## 432  2020-05-15               North West     33
## 433  2020-05-16               North West     30
## 434  2020-05-17               North West     23
## 435  2020-05-18               North West     28
## 436  2020-05-19               North West     33
## 437  2020-05-20               North West     23
## 438  2020-05-21               North West     23
## 439  2020-05-22               North West     24
## 440  2020-05-23               North West     28
## 441  2020-05-24               North West     24
## 442  2020-05-25               North West     28
## 443  2020-05-26               North West     24
## 444  2020-05-27               North West     17
## 445  2020-05-28               North West      8
## 446  2020-03-01               South East      0
## 447  2020-03-02               South East      0
## 448  2020-03-03               South East      1
## 449  2020-03-04               South East      0
## 450  2020-03-05               South East      1
## 451  2020-03-06               South East      0
## 452  2020-03-07               South East      0
## 453  2020-03-08               South East      1
## 454  2020-03-09               South East      1
## 455  2020-03-10               South East      1
## 456  2020-03-11               South East      1
## 457  2020-03-12               South East      0
## 458  2020-03-13               South East      1
## 459  2020-03-14               South East      1
## 460  2020-03-15               South East      5
## 461  2020-03-16               South East      8
## 462  2020-03-17               South East      7
## 463  2020-03-18               South East     10
## 464  2020-03-19               South East      9
## 465  2020-03-20               South East     13
## 466  2020-03-21               South East      7
## 467  2020-03-22               South East     25
## 468  2020-03-23               South East     20
## 469  2020-03-24               South East     22
## 470  2020-03-25               South East     28
## 471  2020-03-26               South East     34
## 472  2020-03-27               South East     34
## 473  2020-03-28               South East     36
## 474  2020-03-29               South East     54
## 475  2020-03-30               South East     58
## 476  2020-03-31               South East     65
## 477  2020-04-01               South East     65
## 478  2020-04-02               South East     55
## 479  2020-04-03               South East     72
## 480  2020-04-04               South East     80
## 481  2020-04-05               South East     81
## 482  2020-04-06               South East     88
## 483  2020-04-07               South East     99
## 484  2020-04-08               South East     82
## 485  2020-04-09               South East    104
## 486  2020-04-10               South East     88
## 487  2020-04-11               South East     87
## 488  2020-04-12               South East     88
## 489  2020-04-13               South East     83
## 490  2020-04-14               South East     65
## 491  2020-04-15               South East     72
## 492  2020-04-16               South East     56
## 493  2020-04-17               South East     86
## 494  2020-04-18               South East     57
## 495  2020-04-19               South East     69
## 496  2020-04-20               South East     85
## 497  2020-04-21               South East     49
## 498  2020-04-22               South East     54
## 499  2020-04-23               South East     57
## 500  2020-04-24               South East     64
## 501  2020-04-25               South East     50
## 502  2020-04-26               South East     51
## 503  2020-04-27               South East     40
## 504  2020-04-28               South East     40
## 505  2020-04-29               South East     46
## 506  2020-04-30               South East     29
## 507  2020-05-01               South East     37
## 508  2020-05-02               South East     35
## 509  2020-05-03               South East     17
## 510  2020-05-04               South East     35
## 511  2020-05-05               South East     29
## 512  2020-05-06               South East     23
## 513  2020-05-07               South East     25
## 514  2020-05-08               South East     25
## 515  2020-05-09               South East     28
## 516  2020-05-10               South East     19
## 517  2020-05-11               South East     23
## 518  2020-05-12               South East     26
## 519  2020-05-13               South East     18
## 520  2020-05-14               South East     31
## 521  2020-05-15               South East     23
## 522  2020-05-16               South East     19
## 523  2020-05-17               South East     16
## 524  2020-05-18               South East     17
## 525  2020-05-19               South East     12
## 526  2020-05-20               South East     22
## 527  2020-05-21               South East     12
## 528  2020-05-22               South East     16
## 529  2020-05-23               South East     17
## 530  2020-05-24               South East     14
## 531  2020-05-25               South East     12
## 532  2020-05-26               South East     12
## 533  2020-05-27               South East     10
## 534  2020-05-28               South East      2
## 535  2020-03-01               South West      0
## 536  2020-03-02               South West      0
## 537  2020-03-03               South West      0
## 538  2020-03-04               South West      0
## 539  2020-03-05               South West      0
## 540  2020-03-06               South West      0
## 541  2020-03-07               South West      0
## 542  2020-03-08               South West      0
## 543  2020-03-09               South West      0
## 544  2020-03-10               South West      0
## 545  2020-03-11               South West      1
## 546  2020-03-12               South West      0
## 547  2020-03-13               South West      0
## 548  2020-03-14               South West      1
## 549  2020-03-15               South West      0
## 550  2020-03-16               South West      0
## 551  2020-03-17               South West      2
## 552  2020-03-18               South West      2
## 553  2020-03-19               South West      5
## 554  2020-03-20               South West      3
## 555  2020-03-21               South West      6
## 556  2020-03-22               South West      9
## 557  2020-03-23               South West      9
## 558  2020-03-24               South West      7
## 559  2020-03-25               South West      9
## 560  2020-03-26               South West     11
## 561  2020-03-27               South West     13
## 562  2020-03-28               South West     21
## 563  2020-03-29               South West     18
## 564  2020-03-30               South West     23
## 565  2020-03-31               South West     23
## 566  2020-04-01               South West     22
## 567  2020-04-02               South West     23
## 568  2020-04-03               South West     30
## 569  2020-04-04               South West     42
## 570  2020-04-05               South West     32
## 571  2020-04-06               South West     34
## 572  2020-04-07               South West     39
## 573  2020-04-08               South West     47
## 574  2020-04-09               South West     24
## 575  2020-04-10               South West     46
## 576  2020-04-11               South West     43
## 577  2020-04-12               South West     23
## 578  2020-04-13               South West     26
## 579  2020-04-14               South West     24
## 580  2020-04-15               South West     31
## 581  2020-04-16               South West     29
## 582  2020-04-17               South West     33
## 583  2020-04-18               South West     25
## 584  2020-04-19               South West     31
## 585  2020-04-20               South West     26
## 586  2020-04-21               South West     26
## 587  2020-04-22               South West     22
## 588  2020-04-23               South West     17
## 589  2020-04-24               South West     19
## 590  2020-04-25               South West     15
## 591  2020-04-26               South West     27
## 592  2020-04-27               South West     13
## 593  2020-04-28               South West     17
## 594  2020-04-29               South West     14
## 595  2020-04-30               South West     26
## 596  2020-05-01               South West      6
## 597  2020-05-02               South West      7
## 598  2020-05-03               South West     10
## 599  2020-05-04               South West     16
## 600  2020-05-05               South West     14
## 601  2020-05-06               South West     18
## 602  2020-05-07               South West     16
## 603  2020-05-08               South West      5
## 604  2020-05-09               South West     10
## 605  2020-05-10               South West      5
## 606  2020-05-11               South West      7
## 607  2020-05-12               South West      7
## 608  2020-05-13               South West      7
## 609  2020-05-14               South West      6
## 610  2020-05-15               South West      3
## 611  2020-05-16               South West      4
## 612  2020-05-17               South West      6
## 613  2020-05-18               South West      4
## 614  2020-05-19               South West      6
## 615  2020-05-20               South West      1
## 616  2020-05-21               South West      9
## 617  2020-05-22               South West      6
## 618  2020-05-23               South West      6
## 619  2020-05-24               South West      3
## 620  2020-05-25               South West      7
## 621  2020-05-26               South West     10
## 622  2020-05-27               South West      5
## 623  2020-05-28               South West      1

1.5 Completion date

We extract the completion date from the NHS Pathways file timestamp:


database_date <- attr(x, "timestamp")
database_date
## [1] "2020-05-28"

The completion date of the NHS Pathways data is Thursday 28 May 2020.

1.6 Add variables

We add the following variable:

  • day: an integer representing the number of days from the earliest data reported, used for modelling purposes; the first day is 0

x <- x %>% 
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)),
         nhs_region = gsub(" Of ", " of ", nhs_region),
         nhs_region = gsub(" And ", " and ", nhs_region),
         day = as.integer(date - min(date, na.rm = TRUE)))

1.7 Auxiliary functions

These are functions which will be used further in the analyses.

Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:


## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here

Rsq <- function(x) {
  1 - (x$deviance / x$null.deviance)
}

Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:


## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals

get_r <- function(model) {
  ##  extract coefficients and conf int
  out <- data.frame(r = coef(model))  %>%
    rownames_to_column("var") %>% 
    cbind(confint(model)) %>%
    filter(!grepl("day_of_week", var)) %>% 
    filter(grepl("day", var)) %>%
    rename(lower_95 = "2.5 %",
           upper_95 = "97.5 %") %>%
    mutate(var = sub("day:", "", var))
  
  ## reconstruct values: intercept + region-coefficient
  for (i in 2:nrow(out)) {
    out[i, -1] <- out[1, -1] + out[i, -1]
  }
  
  ## find the name of the intercept, restore regions names
  out <- out %>%
    mutate(nhs_region = model$xlevels$nhs_region) %>%
    select(nhs_region, everything(), -var)
  
  ## find halving times
  halving <- log(0.5) / out[,-1] %>%
    rename(halving_t = r,
           halving_t_lower_95 = lower_95,
           halving_t_upper_95 = upper_95)
  
  ## set halving times with exclusion intervals to NA
  no_halving <- out$lower_95 < 0 & out$upper_95 > 0
  halving[no_halving, ] <- NA_real_
  
  ## return all data
  cbind(out, halving)
  
}

Functions used in the correlation analysis between NHS Pathways reports and deaths:

## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.

getcor <- function(x, ndx) {
  return(cor(x$deaths[ndx],
             x$note_lag[ndx],
             use = "complete.obs",
             method = "pearson"))
}

## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)

getboot <- function(x) {
  result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000), 
                           type = "bca")
  return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
                    r = result$t0,
                    r_low = result$bca[4],
                    r_hi = result$bca[5]))
}

Function to classify the day of the week into weekend, Monday, and the rest:


## Fn to add day of week
day_of_week <- function(df) {
  df %>% 
    dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>% 
    dplyr::mutate(day_of_week = dplyr::case_when(
      day_of_week %in% c("Sat", "Sun") ~ "weekend",
      day_of_week %in% c("Mon") ~ "monday",
      !(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
    ) %>% 
      factor(levels = c("rest_of_week", "monday", "weekend")))
}

Custom color palettes, color scales, and vectors of colors:


pal <- c("#006212",
         "#ae3cab",
         "#00db90",
         "#960c00",
         "#55aaff",
         "#ff7e78",
         "#00388d")

age.pal <- viridis::viridis(3,begin = 0.1, end = 0.7)

3 Comparison with deaths time series

3.1 Outline

We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.

Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.

3.2 Lagged correlation

We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.

First we join the NHS Pathways and death data, and aggregate over all England:

## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max

dth_trunc <- dth %>%
  rename(date = date_report) %>%
  filter(date <= trunc_date) 

## join with notification data
all_data <- x %>% 
  filter(!is.na(nhs_region)) %>%
  group_by(date, nhs_region) %>%
  summarise(count = sum(count, na.rm = T)) %>%
  ungroup %>%
  inner_join(dth_trunc,
             by = c("date","nhs_region"))

all_tot <- all_data %>%
  group_by(date) %>%
  summarise(count = sum(count, na.rm = TRUE),
            deaths = sum(deaths, na.rm = TRUE)) 

We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:


## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
  
  ## lag reports
  summary <- all_tot %>% 
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI
    getboot(.) %>%
    mutate(lag = i)

  lag_cor <- bind_rows(lag_cor, summary)
}

cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
  theme_bw() +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_point() +
  geom_line() +
  labs(x = "Lag between NHS pathways and death data (days)",
       y = "Pearson's correlation") +
  large_txt
cor_vs_lag


l_opt <- which.max(lag_cor$r)

This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.


all_tot <- all_tot %>%
  rename(date_death = date) %>%
  mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
         note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
         date_note = lag(date_death,16))

lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")

summary(lag_mod)
## 
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -4.7028  -2.5398   0.0328   2.0587   7.3570  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.573e+00  5.485e-02  101.61   <2e-16 ***
## note_lag    8.147e-06  5.341e-07   15.25   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 10.5202)
## 
##     Null deviance: 2792.45  on 34  degrees of freedom
## Residual deviance:  345.99  on 33  degrees of freedom
##   (16 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 4

exp(coefficients(lag_mod))
## (Intercept)    note_lag 
##  263.284469    1.000008
exp(confint(lag_mod))
##                  2.5 %     97.5 %
## (Intercept) 236.201540 292.862829
## note_lag      1.000007   1.000009

Rsq(lag_mod)
## [1] 0.8760989

mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])

all_tot_pred <- 
  all_tot %>%
  filter(!is.na(note_lag)) %>%
  mutate(pred = mod_fit$fit,
         pred.se = mod_fit$se.fit,
         low = exp(pred - 1.96*pred.se),
         hi = exp(pred + 1.96*pred.se))


glm_fit <- all_tot_pred %>% 
    filter(!is.na(note_lag)) %>%
  ggplot(aes(x = note_lag, y = deaths)) +
  geom_point() + 
  geom_line(aes(y = exp(pred))) + 
  geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
  theme_bw() +
  labs(y = "Daily number of\ndeaths reported",
       x = "Daily number of NHS Pathways reports") +
  large_txt

glm_fit

4 Supplementary figures

4.1 Serial interval distribution

This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.

SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale, w = 0.5)

SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
                                        meanlog = log(4.7),
                                        sdlog = log(2.9), w = 0.5)

SI_dist1 <- data.frame(x = SI_distribution$r(1e5)) 
SI_dist1 <- count(SI_dist1, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 30, 5)) +
    theme_bw()

SI_dist2 <- data.frame(x = SI_distribution2$r(1e5)) 
SI_dist2 <- count(SI_dist2, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
    theme_bw()


ggpubr::ggarrange(SI_dist1,
                  SI_dist2,
                  nrow = 1,
                  labels = "AUTO") 

4.2 Sensitivity analysis - 7 or 21 days moving window

We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.

First with the 7 days window:

## set moving time window (1/2/3 weeks)
w <- 7

# create empty df
r_all_sliding_7days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
plot_R <- r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_7days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_7days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_7 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

Then with the 21 days window:

## set moving time window (1/2/3 weeks)
w <- 21

# create empty df
r_all_sliding_21days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
# plot
plot_R <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_21days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_21days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_21 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

And we combine both outputs into a single plot:


ggpubr::ggarrange(r_R_7,
                  r_R_21,
                  nrow = 2,
                  labels = "AUTO",
                  common.legend = TRUE,
                  legend = "bottom") 

4.3 Correlation between NHS Pathways reports and deaths by NHS region


lag_cor_reg <- data.frame()

for (i in 0:30) {

  summary <-
    all_data %>%
    group_by(nhs_region) %>%
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI for each region
    group_modify(~getboot(.x)) %>%
    mutate(lag = i)
  
  lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}

cor_vs_lag_reg <- 
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
  geom_point() +
  geom_line() +
  facet_wrap(~nhs_region) +
  scale_color_manual(values = pal) +
  scale_fill_manual(values = pal, guide = F) +  
  theme_bw() +
  labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(override.aes = list(fill = NA)))

cor_vs_lag_reg

5 Export data

We save the tables created during our analysis:


if (!dir.exists("excel_tables")) {
  dir.create("excel_tables")
}


## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")

for (e in tables_to_export) {
  rio::export(get(e),
              file.path("excel_tables",
                        paste0(e, ".xlsx")))
}

## also export result from regression on lagged data 
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))

6 System information

6.1 Outline

The following information documents the system on which the document was compiled.

6.2 System

This provides information on the operating system.

Sys.info()
##                                                                                             sysname 
##                                                                                            "Darwin" 
##                                                                                             release 
##                                                                                            "19.4.0" 
##                                                                                             version 
## "Darwin Kernel Version 19.4.0: Wed Mar  4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64" 
##                                                                                            nodename 
##                                                                                    "Mac-1771.local" 
##                                                                                             machine 
##                                                                                            "x86_64" 
##                                                                                               login 
##                                                                                              "root" 
##                                                                                                user 
##                                                                                            "runner" 
##                                                                                      effective_user 
##                                                                                            "runner"

6.3 R environment

This provides information on the version of R used:

R.version
##                _                           
## platform       x86_64-apple-darwin15.6.0   
## arch           x86_64                      
## os             darwin15.6.0                
## system         x86_64, darwin15.6.0        
## status                                     
## major          3                           
## minor          6.3                         
## year           2020                        
## month          02                          
## day            29                          
## svn rev        77875                       
## language       R                           
## version.string R version 3.6.3 (2020-02-29)
## nickname       Holding the Windsock

6.4 R packages

This provides information on the packages used:

sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggnewscale_0.4.1     ggpubr_0.3.0         lubridate_1.7.8     
##  [4] chngpt_2020.5-21     cyphr_1.1.0          DT_0.13             
##  [7] kableExtra_1.1.0     janitor_2.0.1        remotes_2.1.1       
## [10] projections_0.4.1    earlyR_0.0.1         epitrix_0.2.2       
## [13] distcrete_1.0.3      incidence_1.7.1      rio_0.5.16          
## [16] reshape2_1.4.4       rvest_0.3.5          xml2_1.3.2          
## [19] linelist_0.0.40.9000 forcats_0.5.0        stringr_1.4.0       
## [22] dplyr_1.0.0          purrr_0.3.4          readr_1.3.1         
## [25] tidyr_1.1.0          tibble_3.0.1         ggplot2_3.3.1       
## [28] tidyverse_1.3.0      here_0.1             reportfactory_0.0.5 
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1  selectr_0.4-2     ggsignif_0.6.0    ellipsis_0.3.1   
##  [5] rprojroot_1.3-2   snakecase_0.11.0  fs_1.4.1          rstudioapi_0.11  
##  [9] farver_2.0.3      fansi_0.4.1       splines_3.6.3     knitr_1.28       
## [13] jsonlite_1.6.1    broom_0.5.6       dbplyr_1.4.4      compiler_3.6.3   
## [17] httr_1.4.1        backports_1.1.7   assertthat_0.2.1  Matrix_1.2-18    
## [21] cli_2.0.2         htmltools_0.4.0   prettyunits_1.1.1 tools_3.6.3      
## [25] gtable_0.3.0      glue_1.4.1        Rcpp_1.0.4.6      carData_3.0-4    
## [29] cellranger_1.1.0  vctrs_0.3.0       nlme_3.1-144      matchmaker_0.1.1 
## [33] crosstalk_1.1.0.1 xfun_0.14         ps_1.3.3          openxlsx_4.1.5   
## [37] lifecycle_0.2.0   rstatix_0.5.0     MASS_7.3-51.5     scales_1.1.1     
## [41] hms_0.5.3         sodium_1.1        yaml_2.2.1        curl_4.3         
## [45] gridExtra_2.3     stringi_1.4.6     kyotil_2019.11-22 boot_1.3-24      
## [49] pkgbuild_1.0.8    zip_2.0.4         rlang_0.4.6       pkgconfig_2.0.3  
## [53] evaluate_0.14     lattice_0.20-38   labeling_0.3      htmlwidgets_1.5.1
## [57] cowplot_1.0.0     processx_3.4.2    tidyselect_1.1.0  plyr_1.8.6       
## [61] magrittr_1.5      R6_2.4.1          generics_0.0.2    DBI_1.1.0        
## [65] pillar_1.4.4      haven_2.3.0       foreign_0.8-75    withr_2.2.0      
## [69] mgcv_1.8-31       survival_3.1-8    abind_1.4-5       modelr_0.1.8     
## [73] crayon_1.3.4      car_3.0-8         utf8_1.1.4        rmarkdown_2.1    
## [77] viridis_0.5.1     grid_3.6.3        readxl_1.3.1      data.table_1.12.8
## [81] blob_1.2.1        callr_3.4.3       reprex_0.3.0      digest_0.6.25    
## [85] webshot_0.5.2     munsell_0.5.0     viridisLite_0.3.0